57,562 research outputs found
Automated road extraction from terrestrial based mobile laser scanning system using the GVF snake model
Accurate extraction and reconstruction of route corridor features from geospatial data is a prerequisite to effective management of road networks for engineering, safety and environmental
applications. High quality road geometry and road side features can now be extracted from
dense point cloud LiDAR data, recorded by modern day Mobile Mapping Systems. This valuable
route network information is gaining the attention of road safety and maintenance engineers.
Road points are needed to be correctly identified, classified and extracted from LiDAR data
before reconstructing intrinsic road geometry and road-side infrastructure. In this paper, we
present a method to automatically extract the road from terrestrial based mobile laser scanning
system using the GVF (Gradient Vector Flow) snake model. A snake is an energy minimizing
spline that moves towards the desired feature or object boundary under the influence of internal
forces within the curve itself and external GVF forces derived typically from 2D imaging data by
minimizing certain energy such as edges or high frequency information. In our novel method, we
initialise the snake contours over point cloud data based on the trajectory information produced
by the MMS navigation sub-system. The internal energy term provided to the snake contour is
based on adjusting the intrinsic properties of the curve, such as elasticity and bending, whilst
the GVF energy and constraint energy terms are derived from the LiDAR point cloud attributes.
Our method primarily differs from the traditional snake models in initialisation and in deriving the
energy terms from the 3D LiDAR data
Doctor of Philosophy
dissertationThe Active Traffic and Demand Management (ATDM) initiative aims to integrate various management strategies and control measures so as to achieve the mobility, environment and sustainability goals. To support the active monitoring and management of real-world complex traffic conditions, the first objective of this dissertation is to develop a travel time reliability estimation and prediction methodology that can provide informed decisions for the management and operation agencies and travelers. A systematic modeling framework was developed to consider a corridor with multiple bottlenecks, and a series of close-form formulas was derived to quantify the travel time distribution under both stochastic demand and capacity, with possible on-ramp and off-ramp flow changes. Traffic state estimation techniques are often used to guide operational management decisions, and accurate traffic estimates are critically needed in ATDM applications designed for reducing instability, volatility and emissions in the transportation system. By capturing the essential forward and backward wave propagation characteristics under possible random measurement errors, this dissertation proposes a unified representation with a simple but theoretically sound explanation for traffic observations under free-flow, congested and dynamic transient conditions. This study also presents a linear programming model to quantify the value of traffic measurements, in a heterogeneous data environment with fixed sensors, Bluetooth readers and GPS sensors. It is important to design comprehensive traffic control measures that can systematically address deteriorating congestion and environmental issues. To better evaluate and assess the mobility and environmental benefits of the transportation improvement plans, this dissertation also discusses a cross-resolution modeling framework for integrating a microscopic emission model with the existing mesoscopic traffic simulation model. A simplified car-following model-based vehicle trajectory construction method is used to generate the high-resolution vehicle trajectory profiles and resulting emission output. In addition, this dissertation discusses a number of important issues for a cloud computing-based software system implementation. A prototype of a reliability-based traveler information provision and dissemination system is developed to offer a rich set of travel reliability information for the general public and traffic management and planning organizations
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
Using Machine Learning for Handover Optimization in Vehicular Fog Computing
Smart mobility management would be an important prerequisite for future fog
computing systems. In this research, we propose a learning-based handover
optimization for the Internet of Vehicles that would assist the smooth
transition of device connections and offloaded tasks between fog nodes. To
accomplish this, we make use of machine learning algorithms to learn from
vehicle interactions with fog nodes. Our approach uses a three-layer
feed-forward neural network to predict the correct fog node at a given location
and time with 99.2 % accuracy on a test set. We also implement a dual stacked
recurrent neural network (RNN) with long short-term memory (LSTM) cells capable
of learning the latency, or cost, associated with these service requests. We
create a simulation in JAMScript using a dataset of real-world vehicle
movements to create a dataset to train these networks. We further propose the
use of this predictive system in a smarter request routing mechanism to
minimize the service interruption during handovers between fog nodes and to
anticipate areas of low coverage through a series of experiments and test the
models' performance on a test set
Vehicular Fog Computing Enabled Real-time Collision Warning via Trajectory Calibration
Vehicular fog computing (VFC) has been envisioned as a promising paradigm for
enabling a variety of emerging intelligent transportation systems (ITS).
However, due to inevitable as well as non-negligible issues in wireless
communication, including transmission latency and packet loss, it is still
challenging in implementing safety-critical applications, such as real-time
collision warning in vehicular networks. In this paper, we present a vehicular
fog computing architecture, aiming at supporting effective and real-time
collision warning by offloading computation and communication overheads to
distributed fog nodes. With the system architecture, we further propose a
trajectory calibration based collision warning (TCCW) algorithm along with
tailored communication protocols. Specifically, an application-layer
vehicular-to-infrastructure (V2I) communication delay is fitted by the Stable
distribution with real-world field testing data. Then, a packet loss detection
mechanism is designed. Finally, TCCW calibrates real-time vehicle trajectories
based on received vehicle status including GPS coordinates, velocity,
acceleration, heading direction, as well as the estimation of communication
delay and the detection of packet loss. For performance evaluation, we build
the simulation model and implement conventional solutions including cloud-based
warning and fog-based warning without calibration for comparison. Real-vehicle
trajectories are extracted as the input, and the simulation results demonstrate
that the effectiveness of TCCW in terms of the highest precision and recall in
a wide range of scenarios
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